
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import plotly.express as px


%matplotlib inline


#read csv file
df = pd.read_csv('201902-fordgobike-tripdata.csv')

df.head()


#discover types of data and look for null value
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 183412 entries, 0 to 183411
Data columns (total 16 columns):
 #   Column                   Non-Null Count   Dtype  
---  ------                   --------------   -----  
 0   duration_sec             183412 non-null  int64  
 1   start_time               183412 non-null  object 
 2   end_time                 183412 non-null  object 
 3   start_station_id         183215 non-null  float64
 4   start_station_name       183215 non-null  object 
 5   start_station_latitude   183412 non-null  float64
 6   start_station_longitude  183412 non-null  float64
 7   end_station_id           183215 non-null  float64
 8   end_station_name         183215 non-null  object 
 9   end_station_latitude     183412 non-null  float64
 10  end_station_longitude    183412 non-null  float64
 11  bike_id                  183412 non-null  int64  
 12  user_type                183412 non-null  object 
 13  member_birth_year        175147 non-null  float64
 14  member_gender            175147 non-null  object 
 15  bike_share_for_all_trip  183412 non-null  object 
dtypes: float64(7), int64(2), object(7)
memory usage: 22.4+ MB


#count null value
df.isnull().sum()

duration_sec                  0
start_time                    0
end_time                      0
start_station_id            197
start_station_name          197
start_station_latitude        0
start_station_longitude       0
end_station_id              197
end_station_name            197
end_station_latitude          0
end_station_longitude         0
bike_id                       0
user_type                     0
member_birth_year          8265
member_gender              8265
bike_share_for_all_trip       0
dtype: int64


#summary statistic
df.describe()


df.duplicated().sum()

0


df_new = df.copy()


#delete unrequired columns

df_new = df_new.drop(['start_station_latitude', 'start_station_longitude', 'end_station_latitude', 'end_station_longitude'], axis=1)


df_new.dropna(inplace=True)


#convert data type of(start_time, end_time) to datetime
df_new['start_time'] = pd.to_datetime(df_new['start_time'])
df_new['end_time'] = pd.to_datetime(df_new['end_time'])


#convert data type of (start_station_id , end_station_id, bike_id) to str
col_id = ['start_station_id' , 'end_station_id', 'bike_id']
for i in col_id:
    df_new[i] = df_new[i].astype(str)


#convert data type of (member_birth_year) to int
df_new['member_birth_year'] = df_new['member_birth_year'].astype(int)


#convert data type of (user_type, member_gender) to category
df_new[['user_type', 'member_gender']] = df_new[['user_type', 'member_gender']].astype('category')


df_new.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 12 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174952 non-null  int64         
 1   start_time               174952 non-null  datetime64[ns]
 2   end_time                 174952 non-null  datetime64[ns]
 3   start_station_id         174952 non-null  object        
 4   start_station_name       174952 non-null  object        
 5   end_station_id           174952 non-null  object        
 6   end_station_name         174952 non-null  object        
 7   bike_id                  174952 non-null  object        
 8   user_type                174952 non-null  category      
 9   member_birth_year        174952 non-null  int32         
 10  member_gender            174952 non-null  category      
 11  bike_share_for_all_trip  174952 non-null  object        
dtypes: category(2), datetime64[ns](2), int32(1), int64(1), object(6)
memory usage: 14.3+ MB


df_new.member_birth_year.describe()

count    174952.000000
mean       1984.803135
std          10.118731
min        1878.000000
25%        1980.000000
50%        1987.000000
75%        1992.000000
max        2001.000000
Name: member_birth_year, dtype: float64


#set member_birth_day to 1978
df_new['member_birth_year'] = df_new['member_birth_year'].replace([1878], 1978)


df_new.member_birth_year.describe()

count    174952.000000
mean       1984.803706
std          10.115522
min        1900.000000
25%        1980.000000
50%        1987.000000
75%        1992.000000
max        2001.000000
Name: member_birth_year, dtype: float64


#create duration_min in minutes more clear than seconds
df_new['duration_min'] = df_new['duration_sec'] / 60


df_new.duration_min.describe()

count    174952.000000
mean         11.733379
std          27.370082
min           1.016667
25%           5.383333
50%           8.500000
75%          13.150000
max        1409.133333
Name: duration_min, dtype: float64


#create columns (day, hour) for start and end trip `https://pandas.pydata.org/docs/reference/series.html`
df_new['start_day'] = df_new['start_time'].dt.day_name()
df_new['end_day'] = df_new['end_time'].dt.day_name()
df_new['start_hour'] = df_new['start_time'].dt.hour
df_new['end_hour'] = df_new['end_time'].dt.hour


#creat colum for member's age
df_new['member_age'] = 2019 - df_new['member_birth_year']


df_new.head()


df_new.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 174952 entries, 0 to 183411
Data columns (total 18 columns):
 #   Column                   Non-Null Count   Dtype         
---  ------                   --------------   -----         
 0   duration_sec             174952 non-null  int64         
 1   start_time               174952 non-null  datetime64[ns]
 2   end_time                 174952 non-null  datetime64[ns]
 3   start_station_id         174952 non-null  object        
 4   start_station_name       174952 non-null  object        
 5   end_station_id           174952 non-null  object        
 6   end_station_name         174952 non-null  object        
 7   bike_id                  174952 non-null  object        
 8   user_type                174952 non-null  category      
 9   member_birth_year        174952 non-null  int32         
 10  member_gender            174952 non-null  category      
 11  bike_share_for_all_trip  174952 non-null  object        
 12  duration_min             174952 non-null  float64       
 13  start_day                174952 non-null  object        
 14  end_day                  174952 non-null  object        
 15  start_hour               174952 non-null  int64         
 16  end_hour                 174952 non-null  int64         
 17  member_age               174952 non-null  int32         
dtypes: category(2), datetime64[ns](2), float64(1), int32(2), int64(3), object(8)
memory usage: 21.7+ MB


df_new.to_csv('fordgobike_201902_clean.csv', index=False)


#load clean data
fordbike_2019 = pd.read_csv('fordgobike_201902_clean.csv')
fordbike_2019.describe()


#explore distribution of trip duration in minutes using plotly.express with logscale for x (reference 'https://plotly.com/python-api-reference/plotly.express.html')
px.histogram(data_frame=fordbike_2019, x= 'duration_min', hover_name='duration_min', log_x=True)


#let's use histplot, set xticks till 30 minutes to more interpret
sb.set_style('darkgrid')
plt.figure(figsize=[12,8])
bins = np.arange(0, 30, 1)
ticks = np.arange(0, 32, 2)
labels = ['{}'.format(v) for v in ticks]
sb.histplot(data= fordbike_2019, x= 'duration_min', bins = bins, shrink=0.8)
plt.xticks(ticks, labels)
plt.title('The Average Trip Duration in Minutes', fontsize=15, fontweight='bold')
plt.xlabel('Trip Duration in Minutes', fontsize=11, fontweight='bold')
plt.ylabel('Number of Trips',fontsize=11, fontweight='bold')
plt.axvline(fordbike_2019.duration_min.mean()+3, color='r')
plt.axvline(fordbike_2019.duration_min.mean()-7, color='r');


#plot number of trips starting each day using countplot

sb.set_style('darkgrid')
base_color = sb.color_palette()[0]
day_ordered = ['Monday','Tuesday','Wednesday','Thursday',
            'Friday','Saturday','Sunday']
plt.figure(figsize=(12,8))
sb.countplot(data= fordbike_2019, y = 'start_day', color=base_color, order=day_ordered)
plt.title('Number of Trips Starting Each Day', fontweight='bold', fontsize=14)
plt.ylabel('WeekDays', fontweight='bold', fontsize=12)
plt.xlabel('Count Trips', fontweight='bold', fontsize=12);


#plot number of trips starting each hour using countplot
sb.set_style('darkgrid')
base_color = sb.color_palette()[0]
hour_ordered = fordbike_2019.start_hour.value_counts().index
plt.figure(figsize=(12,8))
sb.countplot(data= fordbike_2019, y = 'start_hour', color=base_color, order=hour_ordered)
plt.title('Number of Trips Starting Each hour', fontweight='bold', fontsize=14)
plt.ylabel('Hours of The Day', fontweight='bold', fontsize=12)
plt.xlabel('Count Trips', fontweight='bold', fontsize=12);


#I will move on to the other features in the data : `user_type` ,  `member_gender`
fig, ax = plt.subplots(nrows=2, figsize=(10,6))
g = sb.countplot(data=fordbike_2019, x='user_type', color=base_color, ax= ax[0])
ax[0].set_xlabel('User Type', fontweight='bold', fontsize=12)
g.set_title('Average Trips for User Type & for Gender of Members', fontsize=16, fontweight='bold')
sb.countplot(data=fordbike_2019, x='member_gender', color=base_color, ax= ax[1])
ax[1].set_xlabel('Gender of Members', fontweight='bold', fontsize=12);


#plot members age using histplot with kernal density estimation(kde), and use log scale
sb.set_style('darkgrid')
plt.figure(figsize=[12,8])
bins = 10 ** np.arange(1.25, np.log10(fordbike_2019.member_age.max())+0.1, 0.1)
ticks = [15, 20, 25, 30, 35, 40, 45, 50, 60, 65, 70, 75, 80, 85, 90, 95, 100]
labels = ['{}'.format(v) for v in ticks]
sb.histplot(data= fordbike_2019, x= 'member_age', bins=bins, kde=True)
plt.xscale('log')
plt.xticks(ticks, labels)
plt.title('Distribution of Members Age', fontsize=15, fontweight='bold')
plt.xlabel('Member Age', fontsize=11, fontweight='bold')
plt.ylabel('Number of Trips',fontsize=11, fontweight='bold');


#plot start_day and duration_min using pointplot
plt.figure(figsize=(12,8))
day_ordered = ['Monday','Tuesday','Wednesday','Thursday',
            'Friday','Saturday','Sunday']
sb.pointplot(data=fordbike_2019, x='start_day', y='duration_min',
             color=base_color, order = day_ordered)
plt.xlabel('WeekDays', fontsize=16, fontweight='bold')
plt.ylabel('Trip Duration in Minutes', fontsize=12, fontweight='bold')
plt.title('Avg Trip Duration in Minutes Each Day', fontsize=16, fontweight='bold');


plt.figure(figsize=(12,8))

sb.barplot(data=fordbike_2019, x='start_hour', y='duration_min', color=base_color)
plt.xlabel('Hour', fontweight='bold', fontsize=12)
plt.ylabel('Trip Duration in Minutes', fontsize=12, fontweight='bold')
plt.title('Avg Trip Duration in Minutes Each Hour', fontsize=16, fontweight='bold');


#set derution_min less than or equal to 60 minutes
duration_65 = fordbike_2019.query('duration_min<=60')


#user_type, member_gender against duration_min using violinplot
fig, ax = plt.subplots(ncols=2, figsize=(10,8))
sb.violinplot(data=duration_65, x='user_type', y='duration_min', ax=ax[0], color=base_color, inner='quartile')
ax[0].set_xlabel('Type of User', fontsize=12, fontweight='bold')

ax[0].set_ylabel('Trip duration in Minutes', fontsize=12, fontweight='bold')

sb.violinplot(data=duration_65, x='member_gender', y='duration_min', ax=ax[1], color=base_color, inner='quartile')
ax[1].set_xlabel('Gender of Members', fontsize=12, fontweight='bold')
ax[1].set_ylabel('Trip duration in Minutes', fontsize=12, fontweight='bold');


plt.figure(figsize=(10,5))
bins = 10 ** np.arange(1.25, np.log10(fordbike_2019.member_age.max())+0.1, 0.1)
ticks = [15, 20, 25, 30, 35, 40, 45, 50, 60, 65, 70, 75, 80, 85, 90, 95, 100]
labels = ['{}'.format(v) for v in ticks]
sb.scatterplot(data=fordbike_2019, x='member_age', y='duration_min', x_bins=bins, alpha=1/20)
plt.xscale('log')
plt.xticks(ticks, labels)
plt.xlabel('Member Age', fontsize=11, fontweight='bold')
plt.ylabel('Trip duration in Minutes', fontsize=12, fontweight='bold')
plt.yscale('log');


g = sb.FacetGrid(data=fordbike_2019,col='user_type', col_wrap = 2, height = 4, aspect=1.5, sharey=False, margin_titles=True)
g.map_dataframe(sb.countplot, 'start_hour', order=fordbike_2019.start_hour.value_counts().index)
g.set_titles(col_template='{col_name}')
for i in range(2):
    g.axes[i].set_xlabel('Hours of The Day', fontsize=12, fontweight='bold')
    g.axes[i].set_ylabel('Number of Trips');


g = sb.FacetGrid(data=fordbike_2019,col='user_type', col_wrap = 2, height = 4, aspect=1.5, sharey=False, margin_titles=True)
day_ordered = ['Monday','Tuesday','Wednesday','Thursday',
            'Friday','Saturday','Sunday']
g.map_dataframe(sb.countplot, 'start_day', order = day_ordered)
g.set_titles(col_template='{col_name}')
for i in range(2):
    g.axes[i].set_xlabel('WeekDays', fontsize=12, fontweight='bold')
    g.axes[i].set_ylabel('Number of Trips');


plt.figure(figsize=(10,6))
sb.countplot(data=fordbike_2019, x='user_type', hue='member_gender')
plt.xlabel('Type of User', fontweight='bold', fontsize=12)

plt.ylabel('Count', fontweight='bold', fontsize=12);


plt.figure(figsize=(12,8))
day_ordered = ['Monday','Tuesday','Wednesday','Thursday',
            'Friday','Saturday','Sunday']
sb.pointplot(data=fordbike_2019, x='start_day', y='duration_min', 
             color=base_color, order = day_ordered,
             hue='user_type')
plt.xlabel('WeekDays', fontsize=16, fontweight='bold')
plt.ylabel('Trip Duration in Minutes', fontsize=12, fontweight='bold')

plt.title('Avg Trip Duration in Minutes Each Day Depening on Type of User', fontsize=16, fontweight='bold');


day_ordered = ['Monday','Tuesday','Wednesday','Thursday',
            'Friday','Saturday','Sunday']
trip_sub = fordbike_2019.query('user_type == "Subscriber"').groupby(['start_day', 'start_hour']).size()
trip_sub = trip_sub.reset_index(name='count').pivot(index='start_day', columns='start_hour', values='count')
trip_sub = trip_sub.reindex(day_ordered)
trip_cus = fordbike_2019.query('user_type == "Customer"').groupby(['start_day', 'start_hour']).size()
trip_cus = trip_cus.reset_index(name='count').pivot(index= 'start_day', columns='start_hour', values='count')
trip_cus = trip_cus.reindex(day_ordered)


plt.figure(figsize=(15,10))

plt.suptitle('Hourly Usage during Weekdays for Customers and Subscribers', fontsize=16, fontweight='bold')

plt.subplot(2,2,1)
sb.heatmap(trip_sub, cmap='rocket_r')
plt.xlabel('Hours of The Day', fontsize=12, fontweight='bold')
plt.title('Subscriber', fontsize=13, fontweight='bold')
plt.ylabel('')


plt.subplot(2,2,2)
sb.heatmap(trip_cus, cmap='rocket_r')
plt.xlabel('Hours of The Day', fontsize=12, fontweight='bold')
plt.title('Customer', fontsize=13, fontweight='bold')
plt.ylabel('');


day_ordered = ['Monday','Tuesday','Wednesday','Thursday',
            'Friday','Saturday','Sunday']
g = sb.FacetGrid(data=fordbike_2019,col='user_type', height = 4, aspect=1.5, sharey=False, margin_titles=True)
g.map_dataframe(sb.countplot, 'start_day', order=day_ordered, hue='member_gender')
g.set_titles(col_template='{col_name}')
g.add_legend();

	duration_sec	start_time	end_time	start_station_id	start_station_name	start_station_latitude	start_station_longitude	end_station_id	end_station_name	end_station_latitude	end_station_longitude	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip
0	52185	2019-02-28 17:32:10.1450	2019-03-01 08:01:55.9750	21.0	Montgomery St BART Station (Market St at 2nd St)	37.789625	-122.400811	13.0	Commercial St at Montgomery St	37.794231	-122.402923	4902	Customer	1984.0	Male	No
1	42521	2019-02-28 18:53:21.7890	2019-03-01 06:42:03.0560	23.0	The Embarcadero at Steuart St	37.791464	-122.391034	81.0	Berry St at 4th St	37.775880	-122.393170	2535	Customer	NaN	NaN	No
2	61854	2019-02-28 12:13:13.2180	2019-03-01 05:24:08.1460	86.0	Market St at Dolores St	37.769305	-122.426826	3.0	Powell St BART Station (Market St at 4th St)	37.786375	-122.404904	5905	Customer	1972.0	Male	No
3	36490	2019-02-28 17:54:26.0100	2019-03-01 04:02:36.8420	375.0	Grove St at Masonic Ave	37.774836	-122.446546	70.0	Central Ave at Fell St	37.773311	-122.444293	6638	Subscriber	1989.0	Other	No
4	1585	2019-02-28 23:54:18.5490	2019-03-01 00:20:44.0740	7.0	Frank H Ogawa Plaza	37.804562	-122.271738	222.0	10th Ave at E 15th St	37.792714	-122.248780	4898	Subscriber	1974.0	Male	Yes

	duration_sec	start_station_id	start_station_latitude	start_station_longitude	end_station_id	end_station_latitude	end_station_longitude	bike_id	member_birth_year
count	183412.000000	183215.000000	183412.000000	183412.000000	183215.000000	183412.000000	183412.000000	183412.000000	175147.000000
mean	726.078435	138.590427	37.771223	-122.352664	136.249123	37.771427	-122.352250	4472.906375	1984.806437
std	1794.389780	111.778864	0.099581	0.117097	111.515131	0.099490	0.116673	1664.383394	10.116689
min	61.000000	3.000000	37.317298	-122.453704	3.000000	37.317298	-122.453704	11.000000	1878.000000
25%	325.000000	47.000000	37.770083	-122.412408	44.000000	37.770407	-122.411726	3777.000000	1980.000000
50%	514.000000	104.000000	37.780760	-122.398285	100.000000	37.781010	-122.398279	4958.000000	1987.000000
75%	796.000000	239.000000	37.797280	-122.286533	235.000000	37.797320	-122.288045	5502.000000	1992.000000
max	85444.000000	398.000000	37.880222	-121.874119	398.000000	37.880222	-121.874119	6645.000000	2001.000000

	duration_sec	start_time	end_time	start_station_id	start_station_name	end_station_id	end_station_name	bike_id	user_type	member_birth_year	member_gender	bike_share_for_all_trip	duration_min	start_day	end_day	start_hour	end_hour	member_age
0	52185	2019-02-28 17:32:10.145	2019-03-01 08:01:55.975	21.0	Montgomery St BART Station (Market St at 2nd St)	13.0	Commercial St at Montgomery St	4902	Customer	1984	Male	No	869.750000	Thursday	Friday	17	8	35
2	61854	2019-02-28 12:13:13.218	2019-03-01 05:24:08.146	86.0	Market St at Dolores St	3.0	Powell St BART Station (Market St at 4th St)	5905	Customer	1972	Male	No	1030.900000	Thursday	Friday	12	5	47
3	36490	2019-02-28 17:54:26.010	2019-03-01 04:02:36.842	375.0	Grove St at Masonic Ave	70.0	Central Ave at Fell St	6638	Subscriber	1989	Other	No	608.166667	Thursday	Friday	17	4	30
4	1585	2019-02-28 23:54:18.549	2019-03-01 00:20:44.074	7.0	Frank H Ogawa Plaza	222.0	10th Ave at E 15th St	4898	Subscriber	1974	Male	Yes	26.416667	Thursday	Friday	23	0	45
5	1793	2019-02-28 23:49:58.632	2019-03-01 00:19:51.760	93.0	4th St at Mission Bay Blvd S	323.0	Broadway at Kearny	5200	Subscriber	1959	Male	No	29.883333	Thursday	Friday	23	0	60

	duration_sec	start_station_id	end_station_id	bike_id	member_birth_year	duration_min	start_hour	end_hour	member_age
count	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000	174952.000000
mean	704.002744	139.002126	136.604486	4482.587555	1984.803706	11.733379	13.456165	13.609533	34.196294
std	1642.204905	111.648819	111.335635	1659.195937	10.115522	27.370082	4.734282	4.748029	10.115522
min	61.000000	3.000000	3.000000	11.000000	1900.000000	1.016667	0.000000	0.000000	18.000000
25%	323.000000	47.000000	44.000000	3799.000000	1980.000000	5.383333	9.000000	9.000000	27.000000
50%	510.000000	104.000000	101.000000	4960.000000	1987.000000	8.500000	14.000000	14.000000	32.000000
75%	789.000000	239.000000	238.000000	5505.000000	1992.000000	13.150000	17.000000	18.000000	39.000000
max	84548.000000	398.000000	398.000000	6645.000000	2001.000000	1409.133333	23.000000	23.000000	119.000000

Ford GoBike System Data¶

by (Hany Y Wasef)¶

Table of Contents¶

Preliminary Wrangling¶

Loading Liberaries¶

Gathering and Assessing Data¶

Cleaning Data¶

Delete unrequired columns¶

Missing values¶

convert data types¶

Fix outlier¶

create new columns¶

finaly save clean data¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

First I will start by looking for the main feature of interst [duration in minute]¶

How long does the average trip take in minutes?¶

When are most trips taken in weekdays?¶

When are most trips taken in terms of time of day?¶

What is the average of members age?¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

How long does the average trip take for each day of the week?¶

How long is the trip in minutes per hour during the day?¶

What is the trip duration in minutes according to the type of user, and the gender of members?¶

Is there a relationship between the age of the member and the duration of the trip?¶

When are most trips taken hourly during the day depending on the type of user?¶

When are most trips taken for each day of the week depending on the type of user?¶

Do male members use more bikes than females by user type?¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

How long does the average trip take for each day of the week depending on the type of user?¶

When are most trips taken hourly during the day for each day depending on the type of user?¶

When are most trips taken hourly during the day for each day depending on the type of user and the gender of member?¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶

Ford GoBike System Data¶

by (Hany Y Wasef)¶

Table of Contents¶

Preliminary Wrangling¶

Loading Liberaries¶

Gathering and Assessing Data¶

There are 183412 individual rides made in bike-share system with 16 features in this data.need to clean and tidy this data¶

Cleaning Data¶

Delete unrequired columns¶

Missing values¶

convert data types¶

Fix outlier¶

create new columns¶

finaly save clean data¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

First I will start by looking for the main feature of interst [duration in minute]¶

How long does the average trip take in minutes?¶

When are most trips taken in weekdays?¶

When are most trips taken in terms of time of day?¶

What is the most common type of bike share user? Is Male members more than females use the bike share?¶

What is the average of members age?¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

How long does the average trip take for each day of the week?¶

How long is the trip in minutes per hour during the day?¶

What is the trip duration in minutes according to the type of user, and the gender of members?¶

Is there a relationship between the age of the member and the duration of the trip?¶

When are most trips taken hourly during the day depending on the type of user?¶

When are most trips taken for each day of the week depending on the type of user?¶

Do male members use more bikes than females by user type?¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

How long does the average trip take for each day of the week depending on the type of user?¶

When are most trips taken hourly during the day for each day depending on the type of user?¶

When are most trips taken hourly during the day for each day depending on the type of user and the gender of member?¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶